{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 32
        },
        "id": "HMwI-ZC4vKYs",
        "outputId": "63d509b3-8a76-4540-ffa1-c8be8aa874e2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "fig = plt.figure()\n",
        "\n",
        "x = [64, 256, 512]\n",
        "y1 = [0.472, 0.511, 0.527]\n",
        "yerr1 = [0.014, 0.015, 0.013]\n",
        "y2 = [0.581, 0.625, 0.631]\n",
        "yerr2 = [0.014 , 0.007, 0.015] "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "fig.set_size_inches(9,4)\n",
        "plt.subplots_adjust(left=0.1, bottom=0.15, right=0.95, top=0.9, wspace=0.2, hspace=0.15)\n",
        "\n",
        "left = 0\n",
        "right = 580\n",
        "xtitle = \"Input Sequence Length\"\n",
        "ax1.axhline(y=0.41, color='#42f578', linestyle='-.', lw=4)\n",
        "(_, caps, _) = ax1.errorbar(x, y1, yerr1, capsize=20, elinewidth=10, linewidth=0, color='#4251f5')\n",
        "for cap in caps:\n",
        "    cap.set_color('red')\n",
        "    cap.set_markeredgewidth(8)\n",
        "\n",
        "ax1.set_xlim(left, right)\n",
        "ax1.set_title(\"44-Topic Classification\")\n",
        "ax1.set_xlabel(xtitle)\n",
        "ax1.set_ylabel(\"Top-1 Accuracy\")\n",
        "ax1.set_xticks(x)\n",
        "ax1.set_ylim(0.4, 0.56)\n",
        "\n",
        "ax2.axhline(y=0.507, color='#42f578', linestyle='-.', lw=4)\n",
        "(_, caps, _) = ax2.errorbar(x, y2, yerr2, capsize=20, elinewidth=10, linewidth=0, color='#4251f5')\n",
        "for cap in caps:\n",
        "    cap.set_color('red')\n",
        "    cap.set_markeredgewidth(8)\n",
        "ax2.set_ylim(0.5, 0.67)\n",
        "ax2.set_xlim(left, right)\n",
        "ax2.set_title(\"8-Topic Classification\")\n",
        "ax2.set_xlabel(xtitle)\n",
        "ax2.set_xticks(x)\n",
        "\n",
        "plt.savefig(\"Figure_1_Appendix.pdf\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "eTwypAcRxILx",
        "outputId": "f239d402-b5aa-4d9e-bd6d-a6ccba81f1ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}